STRUCTURE OF AIDED CLASSIFICATION OF GROUND OBJECTS BY VIDEO OBSERVATION

Abstract

Analysis of classification structure by video observation has been done. It was formulated, that for feature extraction and their classification, normalized hypothesis for object feature detection, taking into account camera orientation and flight height, have being obtained. The system with aided classification based on probabilistic models, such as Bayesian classifier and Markov chain model, is proposed. The applied algorithm was used for detection by only two features related to Binary Large Objects (BLOB) analyses. Classification was done by two main feature parameters: area and center of mass.  Feature vector contains the most informative components and allows the minimization of decision risks. Results have proven the reliability of classification during a number of video frames in the condition of non-full data descriptive space.